Degree | Type | Year | Semester |
---|---|---|---|
4313861 High Energy Physics, Astrophysics and Cosmology | OB | 0 | 1 |
For the Python Bootcamp (part 2), it is highly needed to bring a personal laptop with a running installation of Python 3.9.
For that, install Python 3.9 with the Anaconda installer. In this way, your Python distribution will contain all the associated packages needed for this course.
Follow these steps:
Download Anaconda installer for Python 3.9 here https://www.anaconda.com/download/
Follow the installation instructions - both GUI or terminal versions work fine. If prompted, select the option to add the new anaconda directory to your path.
The use of Linux or Mac is highly recommended.
In this course we will learn how to distill scientific knowledge from experimental data, a process that relies on statistical methods. We will learn the basics concepts of Probability and Statistics (in their Frequentist and Bayesian frameworks). In addition, we will study and practice several particular statistical methods and data analysis techniques usually used in the fields of High Energy Physics, Astrophysics and Cosmology. To that aim, we will learn and practice the use of modern statistics and analysis software tools.
Part 1: Basic concepts on probability, statistics and Monte Carlo techniques
Part 2: Python for Statistics and Data Analysis
Part 3: Parameter estimation, Hypothesis test and Unfolding
Part 4: Bayesian Statistics
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lectures | 56 | 2.24 | 1, 2, 3 |
Study of theory and practical examples | 40 | 1.6 | 1, 2, 4, 3 |
Type: Autonomous | |||
Discussion, workgroups, problem solving | 34 | 1.36 | 1, 2, 4, 3 |
The evaluation will take into account:
For those students not passing the course after the regular evaluation procedure, there will be a recuperation evaluation round consisting also on specific take-home exercises for the different course parts, plus a final, synthesis exam. There will be no threshold mark to be eligible for the recuperation evaluation round, other than the general requirement of having been evaluated at least for a 66% of the total qualification activities in the first round.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Attendance and active participation to the lectures | 5% | 0 | 0 | 1, 2, 3 |
Resolution of a final, synthesis exam | 50% | 50 | 2 | 1, 2, 4, 3 |
Resolution of class exercises | 45% | 45 | 1.8 | 1, 2, 4, 3 |
We will introduce and make use of the Python programming language (see the "Prerequisists" section for installation details).
In particular, we will study and use the following Python libraries: numpy, pandas, matplotlib, scipy and scikit learn.